Faculty.ai Interview Questions
The questions to prepare for Faculty.ai interviews, across all roles. Questions from real interview reports rank first. Updated weekly.
Identify the main pitfalls that can distort A/B test interpretation and explain how to guard against them.
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Outline the first checks to diagnose a sudden drop in a core product metric, starting with data quality, scope, and decomposition.
Approach for handling missing data in an ML data pipeline, including validation, imputation, and safe downstream consumption.
Explain which classification metrics to use and how metric choice depends on the business objective and error tradeoffs.
Explain how the bias-variance tradeoff guides algorithm selection and generalization performance.
Framework for choosing a feature's primary success metric and guardrails before launch.
Explain how to calculate cumulative totals in SQL using window functions, ordering, and optional pre-aggregation.
Analyze weekly service delivery performance by team using joins, a CTE, date aggregation, and KPI calculations.
Deloitte
ReputationUse joins, CTEs, and aggregations to find the weakest step in an onboarding funnel and estimate lost conversions.
Atlassian
RevolutUse a CTE and window functions to calculate daily, cumulative, and three-day moving transaction volumes by Mastercard MCC.
Mastercard